Stationary distribution vs invariant distribution of a Markov chain

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Lets $p$ be a distribution on a finite sample space with $n$ points. I wish to find a transition matrix that is invariant with respect to $p$, that is $$p^T T = p^T$$.

The problem is clearly underspecified since the number of variables ($n^2$) will always be greater (except when $n=2$) than the number of constraints ($2n$).

I was wondering if there any way to characterize the subset of solutions that have $p$ as the stationary distribution as well (and not just invariant with respect to $p$).

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A Markov chain with a finite state space has a unique stationary distribution if and only if it has exactly one closed communicating class; see e.g. these notes and Wikipedia.