*...would the utility of a particular kernel be related to how well it unwraps a particular distribution into a gaussian shape,...*

Given what Anand said, that PCA does not assume any source model on the input space, then I guess the answer is no.

*...and correspondingly does that imply an inescapable tie between kernel choice and problem domain, or might there be a "universal" mapping which tends to map everything into gaussians (i.e., perhaps some of the infinite dimensional mappings have this property?)?*

I think there is a tie between kernel choice and problem domain, but that tie is not necessarily linked to how well it unwraps a particular into a gaussian shape. I am not aware of any precise heuristics for choosing kernels. Maybe someone else does. Experience, or using what has worked well on a particular application before, is usually how kernels are chosen in practice from what I understand.