Suppose $x = 1, 2, 3, 4, 5$ and $y = 2x$ and so Pearson's $r$ is $1$.
Is that invalid because the joint or marginal distributions aren't normal? The issue raised by non-normality is that P-values may be off. But if you want to assess linearity, stick with Pearson. If you are worried about non-normality, use bootstrapping or a permutation test. (If there is a strong correlation, proceed to regression any way and do your inference with a suitable model.)
What's being premised here is that there is a lurking property "correlation strength" and that if one measure is not ideal, we should use another. It is the other way round. Each measure has its own idea of what a strong correlation is. If it is Pearson $r$, it is a linear relationship. If it is Spearman's or Kendall's, it is a monotonic relationship. And there are other measures that purport to find any kind of relationship (departure from independence).
So, the question raises another: What kind of relationships are you interested in? The distributions need to be looked at, but we have had bootstrapping and permutation testing for several decades now, and worries about non-normality need not drive what you do.
Small samples are problematic whatever you do....