Do you find any difference at all between the two models? If y \sim LogNormal(\mu, \sigma) then by definition of the LogNormal distribution, \log y follows a Normal distribution. Both models should be equivalent.
Having said that, I will give a short and a long answer to the question, going a little further from the example.
The sort answer is no. You cannot use the loo to compare the predictive accuracy of both models, as they are measuring this accuracy on different data.
The long answer is that you cannot do it directly, because the log likelihood of the first model is in terms of y whereas for the second one it is in terms of z=\log y. In order to compare them, you must first get the second log likelihood in terms of y too, and then compare. I have just uploaded one example on how to do this to github. It uses this example as validation because as both models are equivalent, if the conversion is done properly, they should both yield the same result, but the explanation and the maths are general and then I apply it to this example. It can be used with different examples.