These problems can usually be solved by writing the set of equations related to the states and the transition probabilities that you have.
Let us call $p_i$ the probability of arriving to the cheese (which is in state 5) from state $i$. We know that by Markov Property, our future is only dependent on our current position, and we can therefore use this as our only variable.
Then we will have the following set:
$$p_1 = \frac{1}{2}p_4 + \frac{1}{2}p_2$$
$$p_2 = \frac{1}{2}p_1 + \frac{1}{2}p_3$$
$$p_3 = \frac{1}{3}p_4 + \frac{1}{3}p_2 + \frac{1}{3}p_6$$
$$p_4 = 0$$
$$p_5 = 1$$
$$p_6 = \frac{1}{3}p_3 + \frac{1}{3}p_5 + \frac{1}{3}p_7$$
$$p_7 = p_6$$
All of these are simply given by the transition probabilities, with the exception of $p_4$ and $p_5$. By definition, the probability of arriving to the cheese if we meet the cat (i.e. we are on 4) is 0, while it is 1 if we are already in state 5.
From here you can solve your linear equation.
EDIT: As previously mentioned, there was a good chance of me making an error in solving the equations. Thank you user2974951 for the pointer, I redid the equation and find $p_2 = \frac{2}{11}$ which is indeed around 18%
Given that the cheese and the cat are the only absorbing states of your Markov Chain, it means that the probability that it finds the cat first is $1-p_2$, which is around 81%.