well, not sure what you mean by "Sequence->structure".
Historically, people have used the fact that small globular proteins refold spontaneously and rapidly to their native state to support the idea that there is a single, unique structure encoded by a specific sequence. That's a helpful if ultimately limited approach (as we observe many proteins that don't fold rapidly to single native structure).
That's a reason that evolution-based methods, which use statistics about families of related proteins to estimate distances between pairs of amino acids (in 3D space), are more effective- many times in biology we can use evolutionary relationships between proteins to infer things that would be hard to determine through experiments or rigorous, thorough simulations.
But it's important to appreciate there are a large number of proteins that don't fold to a single unique structure rapidly-- and there are many ways this can be the case and many different biologically relevant behaviors depend on these properties. The tools from CASP are much less useful for proteins that violate the assumptions of Anfinsen's dogma, although the evolutionary data is helpful there too, it can often be a lot more challenging to deconvolute the signal.
Ultimately, "what is the right problem"? THe one that makes the most money? Produces the most "useful" scientific result? Is accessible with today's technology? For now, there's plenty of value in these sorts of competitions.
Personally I think the "right problem" is: "given a collection of diseases, use experimentally derived data and clever math, to discover biological treatments that reduce the total suffering from those diseases, subject to monetary and ethical constraints". That's what pharma attempts to do, although not particularly well. Others might say simply solving interesting problems like protein folding is inherently valuable as the right problem.