Yes, the PC axes are “comparable”. I think the best way to think about what a PCA does is to interpret it as a projection of a multidimensional space down to a low dimensional space that captures as much of the overall variation as possible. The first axis is somewhat special because it represents the best 1-dimensional space. Past that one should think of 1 and 2 giving the best 2-dimensional space and 1, 2, and 3 giving the best 3-dimensional space, etc. The axes themselves are not of a priori interest in an application – it is the space that is of interest. A consequence is that plots showing projections of points relative to PC1, PC2,etc. must be plotted to the same scale (i.e., consistent with the fact that the eigenvalues give the variances along each axis). If, as unfortunately often the case, the axes are plotted using different scales then the space has been distorted and is no longer the space that best accounts for the overall variation in the data. That also distorts the impressions one gets in looking at the plot as using different scales changes the relative distances between points.