This paper considers the problem of finding near-optimal Markovian randomized (MR) policies for finite-state-action, infinite-horizon, constrained risk-sensitive Markov decision processes (CRSMDPs). Constraints are in form standard expected discounted cost functions as well over finite and infinite horizons. We first show that aforementioned CRSMDP optimization possesses a solution if it is fea...